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City is the main place to consume goods and services throughout the world. Among the various consumption terminals, household-level consumption is highly behavior driven, which can be affected by various factors such as household income level, age, living environment etc. However, city-level household emissions characteristics are still not fully understood due to the complexity of consumption behaviors and the lack of the supply chain's data. To include the environmental responsibility embodied in residential consumption and reveal the how it varies among household type and season, this study investigated city-level household consumption as it relates to energy demand using a cityscale input-output model and urban residential consumption inventories. Importantly, age-and monthbased emission are analyzed from different aspects such as emission type, source, fuel types and consumption items. Findings indicate that 1) household emissions differ substantially among the various household age groups; older households generally produce higher emissions levels on a per 1 The short version of the paper was presented at CUE2018, Jun 5-7, Shanghai, China. This paper is a substantial extension of the short version of the conference paper. 2 capita basis; 2) decreases in temperature are the main reason for the increased emissions in older households, while this is not a significant factor in younger households; 3) the high per capita household emissions in older households indicate inefficient energy usage among elder citizens, which strongly suggests that aging societies will face long-term emissions increases if appropriate measures are not taken.

With the coronavirus pandemic wreathing havoc around the world, power industry has been hit hard due to the proposal of lockdown policies. However, the impact of lockdowns and shutdowns on the power system in different regions as well as periods of the pandemic can hardly be reflected on the foundation of current studies. In this paper, a prediction-based analysis method is developed to point out the electricity consumption gap resulted from the pandemic situation. The core of this method is a novel optimized grey prediction model, namely Rolling IMSGM(1,1) (Rolling Mechanism combined with grey model with initial condition as Maclaurin series), which achieves better prediction results in the face of long-term emergencies. A novel initial condition is adopted to track data with various characteristics in the form of higher-order polynomials, which are then determined by intelligent algorithms to realize accurate fitting. Historical power consumption data in China are utilized to carry out the monthly forecasts during COVID-19. Compared with other competitive models’ prediction results, the superiority of IMSGM(1,1) are demonstrated. Through analyzing the gap between predicted consumption values and the actual data, it can be found that the impact of the pandemic on electricity varies in different periods, which is related to its severity and the local lockdown policies. This study helps to understand the impact on power industry in the face of such an emergency intuitively so as to respond to possible future events.

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